Visualization

First, import and tidy data:

gender neighborhood VS hiv

neb_plot = hiv_data %>% 
  group_by(neighborhood, gender) %>% 
  filter(year != "ALL", borough != "All", neighborhood != "All", gender != "All") %>% 
  filter(age != "All") %>%
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(x = reorder(neighborhood, sum_hiv), y = sum_hiv, color = gender)) + 
  coord_flip() +
  geom_point() +
  labs(
        title = "Gender and Neighborhood Influence on HIV Incidence",
        x = "Neighborhood",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      )

ggplotly(neb_plot)

The number of HIV diagnoses is higher among male than female in all neighborhoods. Beford Stuyvesant - Crown Heights have the most HIV diagnoses for both men and women.

Gender, age vs hiv

• Omit the data of transgender group, because the data of transgender were not divided into specific age groups.

age_plot = hiv_data %>% 
  filter(race == "All" & borough == "All" & age != "All") %>% 
  group_by(gender, age) %>% 
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(y = sum_hiv, x = age, fill = gender)) + 
  geom_bar(stat = "identity", alpha = 0.8, position = position_dodge()) +
  scale_fill_brewer(palette = "Dark2") +
  labs(
        title = "Gender and Age Influence on HIV Incidence",
        x = "Age range",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      ) 

ggplotly(age_plot)

Gender race vs hiv

race_plot = hiv_data %>% 
  filter(age == "All" & borough == "All" & race != "All") %>% 
  group_by(gender, race) %>% 
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(y = sum_hiv, x = reorder(race, sum_hiv), fill = gender)) + 
  geom_bar(stat = "identity", alpha = 0.8, position = position_dodge()) +
  scale_fill_manual(values = c("#E69F00", "#56B4E9")) +
  labs(
        title = "Race and Gender Influence on HIV Incidence",
        x = "Race",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      ) 

ggplotly(race_plot)

hiv diagnoses in borough with most hiv over years

hiv_data %>%
  filter(borough != "All", neighborhood == "All", gender == "All", age == "All", race == "All") %>% 
  group_by(borough) %>% 
  summarize(sum_hiv = sum(hiv_diagnoses)) %>% 
  arrange(desc(sum_hiv))

## # A tibble: 5 x 2
##   borough       sum_hiv
##   <chr>           <int>
## 1 Brooklyn         3815
## 2 Manhattan        3536
## 3 Bronx            2736
## 4 Queens           2327
## 5 Staten Island     217

year_plot = hiv_data %>% 
  mutate(year = as.integer(year)) %>% 
  filter(borough == "Brooklyn" & gender == "Male" & age == "20 - 29") %>% 
  group_by(year, neighborhood) %>% 
  summarize(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(x = year, y = sum_hiv, color = neighborhood)) +
  geom_line()
  
ggplotly(year_plot)

Income vs Hiv

hiv_income = read_csv("./data/combine_hiv_income.csv")

## Warning: Missing column names filled in: 'X1' [1]

## Parsed with column specification:
## cols(
##   .default = col_integer(),
##   uhf = col_character(),
##   borough = col_character(),
##   gender = col_character(),
##   age = col_character(),
##   race = col_character(),
##   hiv_diagnosis_rate = col_double(),
##   aids_diagnosis_rate = col_double(),
##   plwdhi_prevalence = col_double(),
##   death_rate = col_double(),
##   hiv_related_death_rate = col_double(),
##   non_hiv_related_death_rate = col_double()
## )

## See spec(...) for full column specifications.

income_plot = hiv_income %>% 
  filter(year != 2011, gender == "All", age == "All", race == "All") %>% 
  group_by(uhf) %>%
  summarise(sum_hiv = sum(hiv_diagnoses), mean_income = mean(mid_income)) %>% 
  ggplot(aes(x = mean_income, y = sum_hiv)) + 
  geom_point() +
  labs(
        title = "Income Influence on HIV Incidence",
        x = "Average income of each neighborhood",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      )

ggplotly(income_plot)

Limitations:

We can not visualize the effect of age and race at the same time.